Sars-CoV-2 mutations are one of the hottest topics at the moment. New strains of the virus are causing the COVID-19 pandemic to erupt in places that appear to be under control. They can extend the current critical phase beyond the expected time.
A study, conducted at the Glebe Wattagen Institute of Physics, Campinas State University (IFGW-Unicamp), modeled the mutations that the SARS-CoV-2 virus experienced during the reproduction process, and thus the genetic evolution of the virus throughout its epidemic. The data was published in the magazine PLUS ONE.
In the article, the authors emphasize the warning already made by other scientists: populations that have not been vaccinated and social groups that refuse to receive the vaccine favor the emergence of variants. And if this problem is not resolved urgently, the epidemic may reach its peak again on a global scale.
“As is known, viruses are very simple organisms, unable to reproduce on their own. In order to replicate their RNA, they need to use the cells of the host. By damaging them, they cause disease. It turns out that during the transcription process, they cannot Avoid transcription errors More complex organisms have error-correcting mechanisms But viruses do not If any of these errors provide an advantage to the virus in terms of propagation, this mutation will become significant. Ultimately it may be dominant. If non-reproduction occurs without brakes“Because of the lack of vaccination, mutations tend to occur more and more and spread all over the world,” says physicist Marcos de Aguiar, professor at IFGW-Unicamp and coordinator of the study.
Contrary to what deniers say, it is not vaccination that favors mutation. But its absence explains the researcher.
When a large part of the population is vaccinated, the virus stops spreading. A low rate of virus spread reduces the rate of virus reproduction. Hence, the chance of new variants emerging.”
Traditional models of epidemiology focus on numbers of people infected and at risk, and recovery over time. In the current study, the model included the RNA description of the virus. “Knowing how circulating microorganisms differ from original viruses is important for understanding the emergence of new variants. Also to estimate whether a person is infected with the original virus, even if they are already infected with the original virus, a person can become infected again with the variant. However, predicting what Whether or not the new pathogen will be able to escape from the action of the vaccines designed for the original,” Aguiar explains.
As with any scientific model, the model developed in the study is a perfectly simplified approximation of what is actually happening in reality. The base on which it is built is a SEIR-type model, and it has already been established in epidemiology. The acronym SEIR consists of the first letters of four words in the English language: “gallery for“(Exhibit to),”gallery for“(Exhibit to),”intended(contagious) andget well(Retrieved). ‘Sensitive’ is someone who can be infected; ‘exposed’ means infected but not contagious; ‘contagious’ means infected and contagious. ‘Recovered’, a disease that has already recovered from illness and, ideally, can no longer his injury.
“To avoid excessive complexity, which would make the model mathematically impractical, we consider that individuals classified as ‘recovered’ cannot be infected with any variant that may arise. We also consider mutations to be neutral, i.e. they do not confer the mutated virus any advantage or disadvantage. This is not what actually happens in reality. But we have adopted these simplifications so that we can focus on our goal, which is to study the accumulation of viral mutations during an epidemic and how different viruses are,” explains the researcher.
To achieve this goal, the model was added with description of viruses, from RNA, with 29,900 N-bases, mutation rate 0.001 per base per year – data obtained from the structure and behavior of Sars-CoV-2.
As long as the individual remains infected, the virus can mutate and be transmitted. We calculate the “distance” between the original virus and the variant from the number of different nitrogenous bases they have. Our equations indicate that it is possible to predict with epidemiological data [número de suscetíveis, infectados e recuperados], viral population variability [‘distância média’ entre as sequências de RNA], without access to a huge amount of genetic data,” says Aguiar.
In order to test the model, the researchers used equations to show, based on data from the epidemic in China, in early 2020, how the evolution of the “average genetic distance” between viruses that would have hypothetically emerged during that time would be. Comparing the result with the calculated distances from genetic data obtained locally in the same period, the predictions showed good agreement with the real data.
The spread of the virus through privileged communities [cidades, países etc.] It can result in sequences completely different from the original, increasing the chances of reinfection, depending largely on the contact between these communities. The less closely the two communities are related, the greater the difference in the virus that one can transmit to the other. This increases the chance that a virus circulating in one community will be able to escape the control of the immune system of individuals in the other,” summarizes the researcher.
He adds: “It is important to emphasize that in order for the virus to effectively mutate, giving it advantages or disadvantages, it is necessary that the defects of replication occur at specific sites of the viral RNA. Thus, high genetic distances increase the chance of significant mutations, but do not guarantee them. Our considerations are based on this perspective.”
The study received support from FAPESP through a thematic project; on one Regular research assistance awarded to Aguirre; And PhD scholarship From Victor Marchionne Monteiro, instructs de Aguiar and lead author of the article.
Article Modeling neutral viral mutations in the spread of SARS-CoV-2 epidemics It can be accessed at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255438.
This text was originally published by FAPESP . agency according to Licença Creative Commons CC-BY-NC-ND. Read the original here.
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